Adopting TOGAF Framework for Sustainable and Scalable Robusta Coffee Leaf Rust Management
Robusta coffee (Coffea canephora) is a globally significant crop. However, managing Coffee Leaf Rust remains challenging due to the reliance on manual detection methods and the lack of structured technological integration. This study proposes a TOGAF-based framework as a scalable and adaptable solut...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Ital Publication
2025-06-01
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| Series: | Emerging Science Journal |
| Subjects: | |
| Online Access: | https://ijournalse.org/index.php/ESJ/article/view/3013 |
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| Summary: | Robusta coffee (Coffea canephora) is a globally significant crop. However, managing Coffee Leaf Rust remains challenging due to the reliance on manual detection methods and the lack of structured technological integration. This study proposes a TOGAF-based framework as a scalable and adaptable solution for structuring Coffee Leaf Rust management strategies. The framework leverages enterprise architecture principles to integrate learning algorithms, image detection, and systematic plantation mapping within a structured approach that enhances data organization, rust severity visualization, and predictive analysis. The proposed framework provides a strategic roadmap for integrating technology into Coffee Leaf Rust detection and management by focusing on modularity, scalability, and stakeholder engagement. Unlike existing ad-hoc approaches, this framework is a foundation for future technology-driven solutions, balancing manual practices with structured digital adoption. As no prior research has combined TOGAF with agricultural disease management, this study presents a novel conceptual contribution that could guide future developments in smart agriculture. By adopting this framework, the Robusta coffee industry can move toward proactive, data-driven Coffee Leaf Rust management, fostering long-term resilience and productivity. |
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| ISSN: | 2610-9182 |